On-line nonlinear systems identification via dynamic neural networks with multi-time scales

In this paper, an new on-line identification algorithm with dead-zone function is proposed for nonlinear systems identification via dynamic neural networks with different time-scales including the aspects of fast and slow phenomenon. The main contribution of the paper is that the Lyapunov function and singularly perturbed techniques are used to develop the on-line update laws for both dynamic neural networks weights and the linear part parameters of the neural network model. On example is also given to demonstrate the effectiveness of the proposed identification algorithm.